Ensemble subspace, penalty, pretest, and shrinkage strategies for high dimensional data

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There are a host of buzzwords in today’s data-centric world. We encounter data in all walks of life, and for analytically- and objectively-minded people, data is crucial to their goals. Making sense of the data and extracting meaningful information from it may not be an easy task. The growth in the size and scope of data sets in a host of disciplines has created a need for innovative statistical strategies for understanding and analyzing such data. A variety of statistical and computational tools are needed to reveal the story that is contained in the data. We define high dimensional data (HDD) as data sets for which the number of predictors are larger than the sample size. The analysis of HDD is an important feature in a host of research fields such as social media, engineering networks, bio-informatics, environmental, and others. The buzzword “Big Data” is nebulously defined, but its problems are real and statisticians play a vital role in this data world. Undoubtedly, overcoming the challenges of HDD is key to successful research in a host of fields. Many organizations are using sophisticated number-crunching, data mining, or Big Data analytics to reveal patterns based on collected information. Clearly, there is an increasing demand for efficient prediction strategies for analyzing HDD. Some examples of HDD that have prompted demand are gene expression arrays, social network modeling, clinical, genetics and phenotypic data.